Multi-site evaluation of the JULES land surface model using global and local data
This study evaluates the ability of the JULES land surface model (LSM) to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters include site-specific (local) values for each flux tower site and the default parameters used in the Hadley Centre Global Env...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-02-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/8/295/2015/gmd-8-295-2015.pdf |
| _version_ | 1819147179939332096 |
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| author | D. Slevin S. F. B. Tett M. Williams |
| author_facet | D. Slevin S. F. B. Tett M. Williams |
| author_sort | D. Slevin |
| collection | DOAJ |
| description | This study evaluates the ability of the JULES land surface model (LSM)
to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters
include site-specific (local) values for each flux tower site and the default parameters used in
the Hadley Centre Global Environmental Model (HadGEM) climate model. Firstly, gross
primary productivity (GPP) estimates from driving JULES with data derived from local site
measurements were compared to observations from the FLUXNET network. When using local
data, the model is biased with total annual GPP underestimated by
16% across all sites compared to observations.
Secondly, GPP estimates from driving JULES with data derived from global parameter and
atmospheric reanalysis (on scales of 100 km or so) were compared to FLUXNET
observations. It was found that model
performance decreases further, with total annual GPP underestimated by 30% across all sites
compared to observations. When JULES was driven using local parameters and global
meteorological data, it was shown that global data could be used in place of FLUXNET
data with a 7% reduction in total annual simulated GPP. Thirdly, the global
meteorological data sets, WFDEI and PRINCETON, were compared to
local data to find that the WFDEI data set more closely matches
the local meteorological measurements (FLUXNET). Finally, the JULES phenology model was tested
by comparing results from simulations using the
default phenology model to those forced with the remote sensing product
MODIS leaf area index (LAI). Forcing the model with daily satellite LAI results in
only small improvements in predicted GPP at a small number of sites, compared to using the default
phenology model. |
| first_indexed | 2024-12-22T13:25:43Z |
| format | Article |
| id | doaj.art-002726829a3247cd9f140da07e66c084 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-22T13:25:43Z |
| publishDate | 2015-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-002726829a3247cd9f140da07e66c0842022-12-21T18:24:18ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032015-02-018229531610.5194/gmd-8-295-2015Multi-site evaluation of the JULES land surface model using global and local dataD. Slevin0S. F. B. Tett1M. Williams2School of Geosciences, The University of Edinburgh, Grant Institute, James Hutton Road, Edinburgh, EH9 3FE, UKSchool of Geosciences, The University of Edinburgh, Grant Institute, James Hutton Road, Edinburgh, EH9 3FE, UKSchool of Geosciences, The University of Edinburgh, Grant Institute, James Hutton Road, Edinburgh, EH9 3FE, UKThis study evaluates the ability of the JULES land surface model (LSM) to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters include site-specific (local) values for each flux tower site and the default parameters used in the Hadley Centre Global Environmental Model (HadGEM) climate model. Firstly, gross primary productivity (GPP) estimates from driving JULES with data derived from local site measurements were compared to observations from the FLUXNET network. When using local data, the model is biased with total annual GPP underestimated by 16% across all sites compared to observations. Secondly, GPP estimates from driving JULES with data derived from global parameter and atmospheric reanalysis (on scales of 100 km or so) were compared to FLUXNET observations. It was found that model performance decreases further, with total annual GPP underestimated by 30% across all sites compared to observations. When JULES was driven using local parameters and global meteorological data, it was shown that global data could be used in place of FLUXNET data with a 7% reduction in total annual simulated GPP. Thirdly, the global meteorological data sets, WFDEI and PRINCETON, were compared to local data to find that the WFDEI data set more closely matches the local meteorological measurements (FLUXNET). Finally, the JULES phenology model was tested by comparing results from simulations using the default phenology model to those forced with the remote sensing product MODIS leaf area index (LAI). Forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small number of sites, compared to using the default phenology model.http://www.geosci-model-dev.net/8/295/2015/gmd-8-295-2015.pdf |
| spellingShingle | D. Slevin S. F. B. Tett M. Williams Multi-site evaluation of the JULES land surface model using global and local data Geoscientific Model Development |
| title | Multi-site evaluation of the JULES land surface model using global and local data |
| title_full | Multi-site evaluation of the JULES land surface model using global and local data |
| title_fullStr | Multi-site evaluation of the JULES land surface model using global and local data |
| title_full_unstemmed | Multi-site evaluation of the JULES land surface model using global and local data |
| title_short | Multi-site evaluation of the JULES land surface model using global and local data |
| title_sort | multi site evaluation of the jules land surface model using global and local data |
| url | http://www.geosci-model-dev.net/8/295/2015/gmd-8-295-2015.pdf |
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